Visualization and User Interaction Methods for Multiscale Biomedical Data

Vaquero, Ricardo Manuel Millán; Rzepecki, J; Friese, Karl-Ingo; Wolter, Franz-Erich

doi:10.1007/978-1-4471-6275-9_5

Cited by 7 publications

(1 citation statement)

References 62 publications

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“…The novelty of this work lies in the use of the original methodology for selecting visualization models proposed in the framework of this paper, including those being rare in practice, as well as in a comprehensive study of alternative ways of representing various data structures that are used in information technologies for visualization analysis tasks. In addition, the elements of the novelty include a high degree of heterogeneity of the data structure visualization models studied, which were not presented in previously published works in a comprehensive manner as visualization means and methods [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Graph Visualization: Alternative Models Inspired by Bioinformatics

Kolomeets

Desnitsky

Kotenko

et al. 2023

Sensors

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Currently, the methods and means of human–machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization models for various types of data, as well as to develop automation tools for the process of choosing the best visualization model for a specific case. There are many data visualization tools in various application fields, but at the same time, the main difficulty lies in presenting data of an interconnected (node-link) structure, i.e., networks. Typically, a lot of software means use graphs as the most straightforward and versatile models. To facilitate visual analysis, researchers are developing ways to arrange graph elements to make comparing, searching, and navigating data easier. However, in addition to graphs, there are many other visualization models that are less versatile but have the potential to expand the capabilities of the analyst and provide alternative solutions. In this work, we collected a variety of visualization models, which we call alternative models, to demonstrate how different concepts of information representation can be realized. We believe that adapting these models to improve the means of human–machine interaction will help analysts make significant progress in solving the problems researchers face when working with graphs.

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Section: Introductionmentioning

confidence: 99%

Graph Visualization: Alternative Models Inspired by Bioinformatics

Kolomeets

Desnitsky

Kotenko

et al. 2023

Sensors

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Simulation and Visual Analysis of Neuromusculoskeletal Models and Data

Stanev

Moschonas

Votis

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 7: New Methods and Tools for Big DataInternational audienceThis work presents a novel medical decision support system for diseases related to the upper body neuromusculature. The backbone of the system is a simulation engine able to perform both forward and inverse simulation of upper limb motions. In forward mode neural signals are fed to the muscles that perform the corresponding motion. In the inverse mode, a specified motion trajectory is used as input and the neural signals that are the root cause of this particular motion are estimated and investigated. Due to the vast amount of information that results from even simple simulations, the results are presented to the expert using visual analytics metaphors and in particular both embodied and symbolic visualizations. Several use cases are presented so as to demonstrate the analytics potential of the proposed system

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A Semantically Adaptable Integrated Visualization and Natural Exploration of Multi-scale Biomedical Data

Vaquero

Agibetov

Rzepecki

et al. 2015

2015 19th International Conference on Information Visualisation

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The exploration of biomedical data which involves heterogeneous sources coming from different spatial scales and medical domains is a challenging topic in current research. In this work, we combine efforts regarding multi-scale visualization, multimodal interaction and knowledge formalization for the exploration of multi-scale biomedical data. The knowledge formalization stores and organizes the information sources, the integrated visualization captures all relevant information for the domain expertise of the user and the multimodal interaction provides a natural exploration. We present a concrete example of use of the proposed exploratory system designed for a biologist investigating multi-scale pathologies.

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Visualization and User Interaction Methods for Multiscale Biomedical Data

Cited by 7 publications

References 62 publications

Graph Visualization: Alternative Models Inspired by Bioinformatics

Graph Visualization: Alternative Models Inspired by Bioinformatics

Simulation and Visual Analysis of Neuromusculoskeletal Models and Data

A Semantically Adaptable Integrated Visualization and Natural Exploration of Multi-scale Biomedical Data

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